probability_discount {bayesDP}  R Documentation 
Bayesian Discount Prior: Comparison Between Current and Historical Data
Description
probability_discount
can be used to estimate the posterior
probability of the comparison between historical and current data in the
context of a clinical trial with normal (mean) data.
probability_discount
is not used internally but is given for
educational purposes.
Usage
probability_discount(
mu = NULL,
sigma = NULL,
N = NULL,
mu0 = NULL,
sigma0 = NULL,
N0 = NULL,
number_mcmc = 10000,
method = "fixed"
)
Arguments
mu 
scalar. Mean of the current data. 
sigma 
scalar. Standard deviation of the current data. 
N 
scalar. Number of observations of the current data. 
mu0 
scalar. Mean of the historical data. 
sigma0 
scalar. Standard deviation of the historical data. 
N0 
scalar. Number of observations of the historical data. 
number_mcmc 
scalar. Number of Monte Carlo simulations. Default is 10000. 
method 
character. Analysis method. Default value " 
Details
This function is not used internally but is given for educational purposes. Given the inputs, the output is the posterior probability of the comparison between current and historical data in the context of a clinical trial with normal (mean) data.
Value
probability_discount
returns an object of class
"probability_discount".
An object of class probability_discount
contains the following:
p_hat

scalar. The posterior probability of the comparison historical data weight. If
method="mc"
, a vector of posterior probabilities of lengthnumber_mcmc
is returned.
References
Haddad, T., Himes, A., Thompson, L., Irony, T., Nair, R. MDIC Computer Modeling and Simulation working group.(2017) Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. Journal of Biopharmaceutical Statistics, 115.
Examples
probability_discount(
mu = 0, sigma = 1, N = 100,
mu0 = 0.1, sigma0 = 1, N0 = 100
)